Overview

Dataset statistics

Number of variables22
Number of observations8124
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory176.0 B

Variable types

Categorical10
Boolean2
Numeric10

Alerts

balance_transfer is highly overall correlated with portfolio_balance and 1 other fieldsHigh correlation
avg_account_balance is highly overall correlated with portfolio_balance and 1 other fieldsHigh correlation
portfolio_balance is highly overall correlated with balance_transfer and 4 other fieldsHigh correlation
insurance is highly overall correlated with portfolio_balance and 1 other fieldsHigh correlation
sip_investments is highly overall correlated with portfolio_balance and 1 other fieldsHigh correlation
lumpsum_investments is highly overall correlated with balance_transfer and 5 other fieldsHigh correlation
combined_purchase is highly overall correlated with lumpsum_investmentsHigh correlation
home_status is highly imbalanced (76.4%)Imbalance
self_employed is highly imbalanced (62.8%)Imbalance
net_worth is highly imbalanced (51.3%)Imbalance
loan is highly skewed (γ1 = 24.68704289)Skewed
balance_transfer has 3524 (43.4%) zerosZeros
term_deposit has 4587 (56.5%) zerosZeros
avg_account_balance has 2806 (34.5%) zerosZeros
insurance has 1449 (17.8%) zerosZeros
loan has 3837 (47.2%) zerosZeros
sip_investments has 2016 (24.8%) zerosZeros
lumpsum_investments has 171 (2.1%) zerosZeros
combined_purchase has 3824 (47.1%) zerosZeros

Reproduction

Analysis started2023-05-10 19:43:49.948007
Analysis finished2023-05-10 19:44:01.531435
Duration11.58 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

num_children
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
0
4991 
1
1474 
2
1271 
3
 
375
4
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8124
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row1
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 4991
61.4%
1 1474
 
18.1%
2 1271
 
15.6%
3 375
 
4.6%
4 13
 
0.2%

Length

2023-05-10T19:44:01.574855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T19:44:01.671079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4991
61.4%
1 1474
 
18.1%
2 1271
 
15.6%
3 375
 
4.6%
4 13
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 4991
61.4%
1 1474
 
18.1%
2 1271
 
15.6%
3 375
 
4.6%
4 13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4991
61.4%
1 1474
 
18.1%
2 1271
 
15.6%
3 375
 
4.6%
4 13
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4991
61.4%
1 1474
 
18.1%
2 1271
 
15.6%
3 375
 
4.6%
4 13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4991
61.4%
1 1474
 
18.1%
2 1271
 
15.6%
3 375
 
4.6%
4 13
 
0.2%

age_band
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
45-50
1136 
41-45
903 
36-40
895 
55-60
865 
31-35
840 
Other values (7)
3485 

Length

Max length5
Median length5
Mean length4.9155588
Min length3

Characters and Unicode

Total characters39934
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31-35
2nd row45-50
3rd row36-40
4th row31-35
5th row55-60

Common Values

ValueCountFrequency (%)
45-50 1136
14.0%
41-45 903
11.1%
36-40 895
11.0%
55-60 865
10.6%
31-35 840
10.3%
51-55 833
10.3%
26-30 735
9.0%
61-65 700
8.6%
65-70 468
5.8%
22-25 356
 
4.4%
Other values (2) 393
 
4.8%

Length

2023-05-10T19:44:01.761038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45-50 1136
14.0%
41-45 903
11.1%
36-40 895
11.0%
55-60 865
10.6%
31-35 840
10.3%
51-55 833
10.3%
26-30 735
9.0%
61-65 700
8.6%
65-70 468
5.8%
22-25 356
 
4.4%
Other values (2) 393
 
4.8%

Most occurring characters

ValueCountFrequency (%)
5 9768
24.5%
- 7781
19.5%
6 4363
10.9%
0 4099
10.3%
4 3837
 
9.6%
1 3719
 
9.3%
3 3310
 
8.3%
2 1853
 
4.6%
7 811
 
2.0%
+ 343
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31810
79.7%
Dash Punctuation 7781
 
19.5%
Math Symbol 343
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 9768
30.7%
6 4363
13.7%
0 4099
12.9%
4 3837
 
12.1%
1 3719
 
11.7%
3 3310
 
10.4%
2 1853
 
5.8%
7 811
 
2.5%
8 50
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 7781
100.0%
Math Symbol
ValueCountFrequency (%)
+ 343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39934
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 9768
24.5%
- 7781
19.5%
6 4363
10.9%
0 4099
10.3%
4 3837
 
9.6%
1 3719
 
9.3%
3 3310
 
8.3%
2 1853
 
4.6%
7 811
 
2.0%
+ 343
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39934
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 9768
24.5%
- 7781
19.5%
6 4363
10.9%
0 4099
10.3%
4 3837
 
9.6%
1 3719
 
9.3%
3 3310
 
8.3%
2 1853
 
4.6%
7 811
 
2.0%
+ 343
 
0.9%

marital_status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Partner
6164 
Single/Never Married
881 
Divorced/Separated
 
569
Widowed
 
510

Length

Max length20
Median length7
Mean length9.1802068
Min length7

Characters and Unicode

Total characters74580
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPartner
2nd rowPartner
3rd rowPartner
4th rowPartner
5th rowPartner

Common Values

ValueCountFrequency (%)
Partner 6164
75.9%
Single/Never Married 881
 
10.8%
Divorced/Separated 569
 
7.0%
Widowed 510
 
6.3%

Length

2023-05-10T19:44:01.855708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T19:44:01.958979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
partner 6164
68.5%
single/never 881
 
9.8%
married 881
 
9.8%
divorced/separated 569
 
6.3%
widowed 510
 
5.7%

Most occurring characters

ValueCountFrequency (%)
r 16109
21.6%
e 11905
16.0%
a 8183
11.0%
n 7045
9.4%
t 6733
9.0%
P 6164
 
8.3%
d 3039
 
4.1%
i 2841
 
3.8%
S 1450
 
1.9%
/ 1450
 
1.9%
Other values (12) 9661
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61794
82.9%
Uppercase Letter 10455
 
14.0%
Other Punctuation 1450
 
1.9%
Space Separator 881
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 16109
26.1%
e 11905
19.3%
a 8183
13.2%
n 7045
11.4%
t 6733
10.9%
d 3039
 
4.9%
i 2841
 
4.6%
v 1450
 
2.3%
o 1079
 
1.7%
l 881
 
1.4%
Other values (4) 2529
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
P 6164
59.0%
S 1450
 
13.9%
M 881
 
8.4%
N 881
 
8.4%
D 569
 
5.4%
W 510
 
4.9%
Other Punctuation
ValueCountFrequency (%)
/ 1450
100.0%
Space Separator
ValueCountFrequency (%)
881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72249
96.9%
Common 2331
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 16109
22.3%
e 11905
16.5%
a 8183
11.3%
n 7045
9.8%
t 6733
9.3%
P 6164
 
8.5%
d 3039
 
4.2%
i 2841
 
3.9%
S 1450
 
2.0%
v 1450
 
2.0%
Other values (10) 7330
10.1%
Common
ValueCountFrequency (%)
/ 1450
62.2%
881
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 16109
21.6%
e 11905
16.0%
a 8183
11.0%
n 7045
9.4%
t 6733
9.0%
P 6164
 
8.3%
d 3039
 
4.1%
i 2841
 
3.8%
S 1450
 
1.9%
/ 1450
 
1.9%
Other values (12) 9661
13.0%

occupation
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Professional
2586 
Retired
1840 
Secretarial/Admin
1435 
Housewife
984 
Business Manager
781 
Other values (2)
498 

Length

Max length17
Median length16
Mean length11.798498
Min length7

Characters and Unicode

Total characters95851
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProfessional
2nd rowSecretarial/Admin
3rd rowManual Worker
4th rowManual Worker
5th rowHousewife

Common Values

ValueCountFrequency (%)
Professional 2586
31.8%
Retired 1840
22.6%
Secretarial/Admin 1435
17.7%
Housewife 984
 
12.1%
Business Manager 781
 
9.6%
Manual Worker 451
 
5.6%
Student 47
 
0.6%

Length

2023-05-10T19:44:02.046131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T19:44:02.153883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
professional 2586
27.6%
retired 1840
19.7%
secretarial/admin 1435
15.3%
housewife 984
 
10.5%
business 781
 
8.3%
manager 781
 
8.3%
manual 451
 
4.8%
worker 451
 
4.8%
student 47
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 13164
13.7%
i 9061
 
9.5%
r 8979
 
9.4%
s 8499
 
8.9%
a 7920
 
8.3%
o 6607
 
6.9%
n 6081
 
6.3%
l 4472
 
4.7%
f 3570
 
3.7%
t 3369
 
3.5%
Other values (17) 24129
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82393
86.0%
Uppercase Letter 10791
 
11.3%
Other Punctuation 1435
 
1.5%
Space Separator 1232
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13164
16.0%
i 9061
11.0%
r 8979
10.9%
s 8499
10.3%
a 7920
9.6%
o 6607
8.0%
n 6081
7.4%
l 4472
 
5.4%
f 3570
 
4.3%
t 3369
 
4.1%
Other values (7) 10671
13.0%
Uppercase Letter
ValueCountFrequency (%)
P 2586
24.0%
R 1840
17.1%
S 1482
13.7%
A 1435
13.3%
M 1232
11.4%
H 984
 
9.1%
B 781
 
7.2%
W 451
 
4.2%
Other Punctuation
ValueCountFrequency (%)
/ 1435
100.0%
Space Separator
ValueCountFrequency (%)
1232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93184
97.2%
Common 2667
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13164
14.1%
i 9061
9.7%
r 8979
9.6%
s 8499
 
9.1%
a 7920
 
8.5%
o 6607
 
7.1%
n 6081
 
6.5%
l 4472
 
4.8%
f 3570
 
3.8%
t 3369
 
3.6%
Other values (15) 21462
23.0%
Common
ValueCountFrequency (%)
/ 1435
53.8%
1232
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13164
13.7%
i 9061
 
9.5%
r 8979
 
9.4%
s 8499
 
8.9%
a 7920
 
8.3%
o 6607
 
6.9%
n 6081
 
6.3%
l 4472
 
4.7%
f 3570
 
3.7%
t 3369
 
3.5%
Other values (17) 24129
25.2%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Professional
3507 
Retired
1558 
Manual Worker
1222 
Business Manager
891 
Secretarial/Admin
510 
Other values (2)
436 

Length

Max length17
Median length16
Mean length11.779665
Min length7

Characters and Unicode

Total characters95698
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProfessional
2nd rowProfessional
3rd rowManual Worker
4th rowManual Worker
5th rowProfessional

Common Values

ValueCountFrequency (%)
Professional 3507
43.2%
Retired 1558
19.2%
Manual Worker 1222
 
15.0%
Business Manager 891
 
11.0%
Secretarial/Admin 510
 
6.3%
Housewife 422
 
5.2%
Student 14
 
0.2%

Length

2023-05-10T19:44:02.254052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T19:44:02.359147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
professional 3507
34.3%
retired 1558
15.2%
manual 1222
 
11.9%
worker 1222
 
11.9%
business 891
 
8.7%
manager 891
 
8.7%
secretarial/admin 510
 
5.0%
housewife 422
 
4.1%
student 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 11505
12.0%
s 10109
10.6%
r 9420
9.8%
a 8753
9.1%
o 8658
9.0%
i 7398
 
7.7%
n 7035
 
7.4%
l 5239
 
5.5%
f 3929
 
4.1%
P 3507
 
3.7%
Other values (17) 20145
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82328
86.0%
Uppercase Letter 10747
 
11.2%
Space Separator 2113
 
2.2%
Other Punctuation 510
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11505
14.0%
s 10109
12.3%
r 9420
11.4%
a 8753
10.6%
o 8658
10.5%
i 7398
9.0%
n 7035
8.5%
l 5239
6.4%
f 3929
 
4.8%
u 2549
 
3.1%
Other values (7) 7733
9.4%
Uppercase Letter
ValueCountFrequency (%)
P 3507
32.6%
M 2113
19.7%
R 1558
14.5%
W 1222
 
11.4%
B 891
 
8.3%
S 524
 
4.9%
A 510
 
4.7%
H 422
 
3.9%
Space Separator
ValueCountFrequency (%)
2113
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93075
97.3%
Common 2623
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11505
12.4%
s 10109
10.9%
r 9420
10.1%
a 8753
9.4%
o 8658
9.3%
i 7398
7.9%
n 7035
7.6%
l 5239
 
5.6%
f 3929
 
4.2%
P 3507
 
3.8%
Other values (15) 17522
18.8%
Common
ValueCountFrequency (%)
2113
80.6%
/ 510
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11505
12.0%
s 10109
10.6%
r 9420
9.8%
a 8753
9.1%
o 8658
9.0%
i 7398
 
7.7%
n 7035
 
7.4%
l 5239
 
5.5%
f 3929
 
4.1%
P 3507
 
3.7%
Other values (17) 20145
21.1%

home_status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Own Home
7550 
Rent from Council/HA
 
279
Rent Privately
 
205
Live in Parental Hom
 
90

Length

Max length20
Median length8
Mean length8.6964549
Min length8

Characters and Unicode

Total characters70650
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn Home
2nd rowOwn Home
3rd rowRent Privately
4th rowOwn Home
5th rowOwn Home

Common Values

ValueCountFrequency (%)
Own Home 7550
92.9%
Rent from Council/HA 279
 
3.4%
Rent Privately 205
 
2.5%
Live in Parental Hom 90
 
1.1%

Length

2023-05-10T19:44:02.461894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T19:44:02.564520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
own 7550
45.2%
home 7550
45.2%
rent 484
 
2.9%
from 279
 
1.7%
council/ha 279
 
1.7%
privately 205
 
1.2%
live 90
 
0.5%
in 90
 
0.5%
parental 90
 
0.5%
hom 90
 
0.5%

Most occurring characters

ValueCountFrequency (%)
8583
12.1%
n 8493
12.0%
e 8419
11.9%
o 8198
11.6%
H 7919
11.2%
m 7919
11.2%
O 7550
10.7%
w 7550
10.7%
t 779
 
1.1%
i 664
 
0.9%
Other values (14) 4576
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44892
63.5%
Uppercase Letter 16896
 
23.9%
Space Separator 8583
 
12.1%
Other Punctuation 279
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 8493
18.9%
e 8419
18.8%
o 8198
18.3%
m 7919
17.6%
w 7550
16.8%
t 779
 
1.7%
i 664
 
1.5%
r 574
 
1.3%
l 574
 
1.3%
a 385
 
0.9%
Other values (5) 1337
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
H 7919
46.9%
O 7550
44.7%
R 484
 
2.9%
P 295
 
1.7%
A 279
 
1.7%
C 279
 
1.7%
L 90
 
0.5%
Space Separator
ValueCountFrequency (%)
8583
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61788
87.5%
Common 8862
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 8493
13.7%
e 8419
13.6%
o 8198
13.3%
H 7919
12.8%
m 7919
12.8%
O 7550
12.2%
w 7550
12.2%
t 779
 
1.3%
i 664
 
1.1%
r 574
 
0.9%
Other values (12) 3723
6.0%
Common
ValueCountFrequency (%)
8583
96.9%
/ 279
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8583
12.1%
n 8493
12.0%
e 8419
11.9%
o 8198
11.6%
H 7919
11.2%
m 7919
11.2%
O 7550
10.7%
w 7550
10.7%
t 779
 
1.1%
i 664
 
0.9%
Other values (14) 4576
6.5%

family_income
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
>=35,000
2122 
<27,500, >=25,000
969 
<30,000, >=27,500
796 
<25,000, >=22,500
656 
<12,500, >=10,000
535 
Other values (7)
3046 

Length

Max length17
Median length17
Mean length14.362383
Min length7

Characters and Unicode

Total characters116680
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row>=35,000
2nd row>=35,000
3rd row<22,500, >=20,000
4th row<25,000, >=22,500
5th row>=35,000

Common Values

ValueCountFrequency (%)
>=35,000 2122
26.1%
<27,500, >=25,000 969
11.9%
<30,000, >=27,500 796
 
9.8%
<25,000, >=22,500 656
 
8.1%
<12,500, >=10,000 535
 
6.6%
<20,000, >=17,500 525
 
6.5%
<17,500, >=15,000 521
 
6.4%
<15,000, >=12,500 508
 
6.3%
<22,500, >=20,000 479
 
5.9%
<10,000, >= 8,000 452
 
5.6%
Other values (2) 561
 
6.9%

Length

2023-05-10T19:44:02.647080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
35,000 2122
13.9%
27,500 1765
11.6%
25,000 1625
10.7%
1341
8.8%
22,500 1135
7.5%
17,500 1046
6.9%
12,500 1043
6.8%
15,000 1029
6.8%
20,000 1004
6.6%
10,000 987
6.5%
Other values (3) 2137
14.0%

Most occurring characters

ValueCountFrequency (%)
0 39477
33.8%
, 19662
16.9%
5 9765
 
8.4%
> 7891
 
6.8%
= 7891
 
6.8%
2 7707
 
6.6%
7110
 
6.1%
< 6002
 
5.1%
1 4105
 
3.5%
3 2918
 
2.5%
Other values (3) 4152
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68124
58.4%
Math Symbol 21784
 
18.7%
Other Punctuation 19662
 
16.9%
Space Separator 7110
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39477
57.9%
5 9765
 
14.3%
2 7707
 
11.3%
1 4105
 
6.0%
3 2918
 
4.3%
7 2811
 
4.1%
8 780
 
1.1%
4 561
 
0.8%
Math Symbol
ValueCountFrequency (%)
> 7891
36.2%
= 7891
36.2%
< 6002
27.6%
Other Punctuation
ValueCountFrequency (%)
, 19662
100.0%
Space Separator
ValueCountFrequency (%)
7110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 116680
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39477
33.8%
, 19662
16.9%
5 9765
 
8.4%
> 7891
 
6.8%
= 7891
 
6.8%
2 7707
 
6.6%
7110
 
6.1%
< 6002
 
5.1%
1 4105
 
3.5%
3 2918
 
2.5%
Other values (3) 4152
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39477
33.8%
, 19662
16.9%
5 9765
 
8.4%
> 7891
 
6.8%
= 7891
 
6.8%
2 7707
 
6.6%
7110
 
6.1%
< 6002
 
5.1%
1 4105
 
3.5%
3 2918
 
2.5%
Other values (3) 4152
 
3.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
False
7543 
True
 
581
ValueCountFrequency (%)
False 7543
92.8%
True 581
 
7.2%
2023-05-10T19:44:02.738426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
False
7207 
True
917 
ValueCountFrequency (%)
False 7207
88.7%
True 917
 
11.3%
2023-05-10T19:44:02.821165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

year_last_moved
Real number (ℝ)

Distinct93
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8381
Minimum1901
Maximum1999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-05-10T19:44:02.915001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1901
5-th percentile1963
Q11978
median1988
Q31994
95-th percentile1998
Maximum1999
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.414802
Coefficient of variation (CV)0.0062548184
Kurtosis5.7086875
Mean1984.8381
Median Absolute Deviation (MAD)7
Skewness-1.7681977
Sum16124825
Variance154.12731
MonotonicityNot monotonic
2023-05-10T19:44:03.028377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1997 617
 
7.6%
1996 525
 
6.5%
1998 434
 
5.3%
1994 423
 
5.2%
1995 386
 
4.8%
1988 338
 
4.2%
1993 323
 
4.0%
1986 316
 
3.9%
1992 294
 
3.6%
1987 284
 
3.5%
Other values (83) 4184
51.5%
ValueCountFrequency (%)
1901 2
< 0.1%
1902 2
< 0.1%
1903 1
 
< 0.1%
1904 2
< 0.1%
1905 3
< 0.1%
1906 1
 
< 0.1%
1907 2
< 0.1%
1908 3
< 0.1%
1909 1
 
< 0.1%
1910 2
< 0.1%
ValueCountFrequency (%)
1999 51
 
0.6%
1998 434
5.3%
1997 617
7.6%
1996 525
6.5%
1995 386
4.8%
1994 423
5.2%
1993 323
4.0%
1992 294
3.6%
1991 256
3.2%
1990 273
3.4%

balance_transfer
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1860
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.082922
Minimum0
Maximum2951.76
Zeros3524
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-05-10T19:44:03.141765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17.485
Q364.99
95-th percentile186.9005
Maximum2951.76
Range2951.76
Interquartile range (IQR)64.99

Descriptive statistics

Standard deviation79.084692
Coefficient of variation (CV)1.7161388
Kurtosis231.64235
Mean46.082922
Median Absolute Deviation (MAD)17.485
Skewness8.1737344
Sum374377.66
Variance6254.3886
MonotonicityNot monotonic
2023-05-10T19:44:03.249531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3524
43.4%
0.01 155
 
1.9%
24.99 135
 
1.7%
29.99 114
 
1.4%
19.99 72
 
0.9%
34.99 63
 
0.8%
25.99 52
 
0.6%
0.51 44
 
0.5%
44.99 43
 
0.5%
0.02 41
 
0.5%
Other values (1850) 3881
47.8%
ValueCountFrequency (%)
0 3524
43.4%
0.01 155
 
1.9%
0.02 41
 
0.5%
0.03 12
 
0.1%
0.04 2
 
< 0.1%
0.05 5
 
0.1%
0.46 1
 
< 0.1%
0.51 44
 
0.5%
0.52 35
 
0.4%
0.53 9
 
0.1%
ValueCountFrequency (%)
2951.76 1
< 0.1%
860.83 1
< 0.1%
749.38 1
< 0.1%
659.21 1
< 0.1%
644.87 1
< 0.1%
601.85 1
< 0.1%
596.85 1
< 0.1%
583.87 1
< 0.1%
573.4 1
< 0.1%
570.86 1
< 0.1%

term_deposit
Real number (ℝ)

Distinct1215
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.284649
Minimum0
Maximum784.82
Zeros4587
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-05-10T19:44:03.367117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q334.49
95-th percentile124.957
Maximum784.82
Range784.82
Interquartile range (IQR)34.49

Descriptive statistics

Standard deviation54.133537
Coefficient of variation (CV)1.9840291
Kurtosis28.68558
Mean27.284649
Median Absolute Deviation (MAD)0
Skewness4.1741626
Sum221660.49
Variance2930.4398
MonotonicityNot monotonic
2023-05-10T19:44:03.479962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4587
56.5%
24.99 128
 
1.6%
29.99 125
 
1.5%
19.99 87
 
1.1%
14.99 75
 
0.9%
9.99 64
 
0.8%
34.99 62
 
0.8%
0.01 58
 
0.7%
29.49 47
 
0.6%
24.49 40
 
0.5%
Other values (1205) 2851
35.1%
ValueCountFrequency (%)
0 4587
56.5%
0.01 58
 
0.7%
0.02 12
 
0.1%
0.03 1
 
< 0.1%
0.51 28
 
0.3%
0.52 10
 
0.1%
0.53 1
 
< 0.1%
1.02 1
 
< 0.1%
1.03 3
 
< 0.1%
1.6 1
 
< 0.1%
ValueCountFrequency (%)
784.82 1
< 0.1%
738.67 1
< 0.1%
716.12 1
< 0.1%
597.76 1
< 0.1%
539.18 1
< 0.1%
522.77 1
< 0.1%
514.26 1
< 0.1%
505.54 1
< 0.1%
493.78 1
< 0.1%
484.73 1
< 0.1%

avg_account_balance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1923
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.838023
Minimum0
Maximum626.24
Zeros2806
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-05-10T19:44:03.599863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14.98
Q345.9225
95-th percentile120.692
Maximum626.24
Range626.24
Interquartile range (IQR)45.9225

Descriptive statistics

Standard deviation45.24944
Coefficient of variation (CV)1.421239
Kurtosis12.495594
Mean31.838023
Median Absolute Deviation (MAD)14.98
Skewness2.6899852
Sum258652.1
Variance2047.5118
MonotonicityNot monotonic
2023-05-10T19:44:03.704881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2806
34.5%
29.99 112
 
1.4%
4.99 111
 
1.4%
11.99 96
 
1.2%
9.99 84
 
1.0%
14.99 74
 
0.9%
24.99 69
 
0.8%
2.99 49
 
0.6%
34.99 48
 
0.6%
14.49 41
 
0.5%
Other values (1913) 4634
57.0%
ValueCountFrequency (%)
0 2806
34.5%
0.01 27
 
0.3%
0.02 4
 
< 0.1%
0.05 1
 
< 0.1%
0.47 1
 
< 0.1%
0.51 9
 
0.1%
0.52 2
 
< 0.1%
0.97 1
 
< 0.1%
0.98 1
 
< 0.1%
1 1
 
< 0.1%
ValueCountFrequency (%)
626.24 1
< 0.1%
415.14 1
< 0.1%
410.71 1
< 0.1%
402.6 1
< 0.1%
398.06 1
< 0.1%
380.75 1
< 0.1%
367.34 1
< 0.1%
351.38 1
< 0.1%
348.18 1
< 0.1%
347.79 1
< 0.1%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Female
6106 
Male
1987 
Unknown
 
31

Length

Max length7
Median length6
Mean length5.514648
Min length4

Characters and Unicode

Total characters44801
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 6106
75.2%
Male 1987
 
24.5%
Unknown 31
 
0.4%

Length

2023-05-10T19:44:03.805677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T19:44:03.904826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
female 6106
75.2%
male 1987
 
24.5%
unknown 31
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 14199
31.7%
a 8093
18.1%
l 8093
18.1%
F 6106
13.6%
m 6106
13.6%
M 1987
 
4.4%
n 93
 
0.2%
U 31
 
0.1%
k 31
 
0.1%
o 31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36677
81.9%
Uppercase Letter 8124
 
18.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14199
38.7%
a 8093
22.1%
l 8093
22.1%
m 6106
16.6%
n 93
 
0.3%
k 31
 
0.1%
o 31
 
0.1%
w 31
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
F 6106
75.2%
M 1987
 
24.5%
U 31
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 44801
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14199
31.7%
a 8093
18.1%
l 8093
18.1%
F 6106
13.6%
m 6106
13.6%
M 1987
 
4.4%
n 93
 
0.2%
U 31
 
0.1%
k 31
 
0.1%
o 31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14199
31.7%
a 8093
18.1%
l 8093
18.1%
F 6106
13.6%
m 6106
13.6%
M 1987
 
4.4%
n 93
 
0.2%
U 31
 
0.1%
k 31
 
0.1%
o 31
 
0.1%

region
Categorical

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
South East
1680 
North West
1517 
Unknown
856 
South West
769 
West Midlands
658 
Other values (8)
2644 

Length

Max length16
Median length15
Mean length9.5942885
Min length5

Characters and Unicode

Total characters77944
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth West
2nd rowNorth West
3rd rowSouth West
4th rowScotland
5th rowUnknown

Common Values

ValueCountFrequency (%)
South East 1680
20.7%
North West 1517
18.7%
Unknown 856
10.5%
South West 769
9.5%
West Midlands 658
 
8.1%
East Midlands 623
 
7.7%
Scotland 615
 
7.6%
North 464
 
5.7%
Wales 443
 
5.5%
East Anglia 344
 
4.2%
Other values (3) 155
 
1.9%

Length

2023-05-10T19:44:03.985881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
west 2944
21.2%
east 2647
19.1%
south 2449
17.6%
north 1981
14.3%
midlands 1281
9.2%
unknown 856
 
6.2%
scotland 615
 
4.4%
wales 443
 
3.2%
anglia 344
 
2.5%
northern 135
 
1.0%
Other values (6) 190
 
1.4%

Most occurring characters

ValueCountFrequency (%)
t 10771
13.8%
s 7340
 
9.4%
o 6051
 
7.8%
5761
 
7.4%
a 5490
 
7.0%
n 5108
 
6.6%
h 4570
 
5.9%
e 3677
 
4.7%
W 3387
 
4.3%
d 3317
 
4.3%
Other values (17) 22472
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58313
74.8%
Uppercase Letter 13870
 
17.8%
Space Separator 5761
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 10771
18.5%
s 7340
12.6%
o 6051
10.4%
a 5490
9.4%
n 5108
8.8%
h 4570
7.8%
e 3677
 
6.3%
d 3317
 
5.7%
l 2843
 
4.9%
u 2449
 
4.2%
Other values (7) 6697
11.5%
Uppercase Letter
ValueCountFrequency (%)
W 3387
24.4%
S 3064
22.1%
E 2647
19.1%
N 2116
15.3%
M 1296
 
9.3%
U 856
 
6.2%
A 344
 
2.5%
I 155
 
1.1%
C 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72183
92.6%
Common 5761
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 10771
14.9%
s 7340
 
10.2%
o 6051
 
8.4%
a 5490
 
7.6%
n 5108
 
7.1%
h 4570
 
6.3%
e 3677
 
5.1%
W 3387
 
4.7%
d 3317
 
4.6%
S 3064
 
4.2%
Other values (16) 19408
26.9%
Common
ValueCountFrequency (%)
5761
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 10771
13.8%
s 7340
 
9.4%
o 6051
 
7.8%
5761
 
7.4%
a 5490
 
7.0%
n 5108
 
6.6%
h 4570
 
5.9%
e 3677
 
4.7%
W 3387
 
4.3%
d 3317
 
4.3%
Other values (17) 22472
28.8%

portfolio_balance
Real number (ℝ)

Distinct6884
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.353368
Minimum-78.43
Maximum4283.56
Zeros0
Zeros (%)0.0%
Negative852
Negative (%)10.5%
Memory size63.6 KiB
2023-05-10T19:44:04.087896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-78.43
5-th percentile-13.54
Q126.2775
median65.56
Q3123.97
95-th percentile271.5595
Maximum4283.56
Range4361.99
Interquartile range (IQR)97.6925

Descriptive statistics

Standard deviation108.30354
Coefficient of variation (CV)1.2120812
Kurtosis283.27618
Mean89.353368
Median Absolute Deviation (MAD)45.695
Skewness8.8954715
Sum725906.76
Variance11729.656
MonotonicityNot monotonic
2023-05-10T19:44:04.192892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.45 5
 
0.1%
57.9 5
 
0.1%
8.89 4
 
< 0.1%
23.38 4
 
< 0.1%
4.09 4
 
< 0.1%
51.05 4
 
< 0.1%
89.12 4
 
< 0.1%
118.52 4
 
< 0.1%
30 4
 
< 0.1%
102.64 4
 
< 0.1%
Other values (6874) 8082
99.5%
ValueCountFrequency (%)
-78.43 1
< 0.1%
-77.23 1
< 0.1%
-76.35 1
< 0.1%
-73.35 1
< 0.1%
-72.74 1
< 0.1%
-69.38 1
< 0.1%
-67.42 1
< 0.1%
-66.27 1
< 0.1%
-64.3 1
< 0.1%
-64.16 1
< 0.1%
ValueCountFrequency (%)
4283.56 1
< 0.1%
1097.44 1
< 0.1%
1053.8 1
< 0.1%
1024.68 1
< 0.1%
952.49 1
< 0.1%
862.32 1
< 0.1%
844.24 1
< 0.1%
790.83 1
< 0.1%
769.02 1
< 0.1%
763.22 1
< 0.1%

net_worth
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
2
7264 
1
860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8124
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Length

2023-05-10T19:44:04.280955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T19:44:04.368493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Most occurring characters

ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

insurance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3605
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.144135
Minimum0
Maximum2952.37
Zeros1449
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-05-10T19:44:04.458186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.99
median49.97
Q3115.835
95-th percentile284.404
Maximum2952.37
Range2952.37
Interquartile range (IQR)101.845

Descriptive statistics

Standard deviation107.78553
Coefficient of variation (CV)1.2809631
Kurtosis69.51097
Mean84.144135
Median Absolute Deviation (MAD)46.48
Skewness4.5115692
Sum683586.95
Variance11617.72
MonotonicityNot monotonic
2023-05-10T19:44:04.563227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1449
 
17.8%
9.99 59
 
0.7%
0.01 44
 
0.5%
29.99 44
 
0.5%
19.99 37
 
0.5%
34.99 31
 
0.4%
24.99 31
 
0.4%
27.99 30
 
0.4%
11.99 29
 
0.4%
22.99 29
 
0.4%
Other values (3595) 6341
78.1%
ValueCountFrequency (%)
0 1449
17.8%
0.01 44
 
0.5%
0.02 16
 
0.2%
0.03 2
 
< 0.1%
0.08 1
 
< 0.1%
0.51 18
 
0.2%
0.52 12
 
0.1%
0.53 5
 
0.1%
0.55 1
 
< 0.1%
0.58 1
 
< 0.1%
ValueCountFrequency (%)
2952.37 1
< 0.1%
1101 1
< 0.1%
899.05 1
< 0.1%
895.52 1
< 0.1%
882.41 1
< 0.1%
866.08 1
< 0.1%
834.37 1
< 0.1%
816.13 1
< 0.1%
806.07 1
< 0.1%
799.59 1
< 0.1%

loan
Real number (ℝ)

SKEWED  ZEROS 

Distinct2105
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.832744
Minimum0
Maximum4905.93
Zeros3837
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-05-10T19:44:04.897437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.49
Q329.325
95-th percentile141.3155
Maximum4905.93
Range4905.93
Interquartile range (IQR)29.325

Descriptive statistics

Standard deviation86.938714
Coefficient of variation (CV)2.9142044
Kurtosis1242.3473
Mean29.832744
Median Absolute Deviation (MAD)3.49
Skewness24.687043
Sum242361.21
Variance7558.3399
MonotonicityNot monotonic
2023-05-10T19:44:05.013337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3837
47.2%
4.99 75
 
0.9%
9.99 48
 
0.6%
14.99 40
 
0.5%
5.99 38
 
0.5%
4.49 34
 
0.4%
3.99 33
 
0.4%
6.99 32
 
0.4%
15.99 31
 
0.4%
10.99 28
 
0.3%
Other values (2095) 3928
48.4%
ValueCountFrequency (%)
0 3837
47.2%
0.01 21
 
0.3%
0.02 6
 
0.1%
0.51 2
 
< 0.1%
0.52 1
 
< 0.1%
0.74 1
 
< 0.1%
0.99 5
 
0.1%
1 5
 
0.1%
1.03 1
 
< 0.1%
1.19 1
 
< 0.1%
ValueCountFrequency (%)
4905.93 1
< 0.1%
1323.56 1
< 0.1%
1280.2 1
< 0.1%
1229.64 1
< 0.1%
898.39 1
< 0.1%
835.22 1
< 0.1%
777.24 1
< 0.1%
771.21 1
< 0.1%
728.13 1
< 0.1%
687.37 1
< 0.1%

sip_investments
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2711
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.780874
Minimum0
Maximum2570.75
Zeros2016
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-05-10T19:44:05.121223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median27.975
Q366.4125
95-th percentile166.837
Maximum2570.75
Range2570.75
Interquartile range (IQR)66.4025

Descriptive statistics

Standard deviation68.729669
Coefficient of variation (CV)1.4384347
Kurtosis234.39128
Mean47.780874
Median Absolute Deviation (MAD)27.975
Skewness8.3986552
Sum388171.82
Variance4723.7674
MonotonicityNot monotonic
2023-05-10T19:44:05.233577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2016
 
24.8%
11.99 89
 
1.1%
9.99 70
 
0.9%
1 49
 
0.6%
19.99 49
 
0.6%
23.98 48
 
0.6%
13.99 48
 
0.6%
19.98 38
 
0.5%
4.99 37
 
0.5%
11.49 37
 
0.5%
Other values (2701) 5643
69.5%
ValueCountFrequency (%)
0 2016
24.8%
0.01 24
 
0.3%
0.02 12
 
0.1%
0.03 2
 
< 0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.48 1
 
< 0.1%
0.5 3
 
< 0.1%
0.51 15
 
0.2%
0.52 5
 
0.1%
ValueCountFrequency (%)
2570.75 1
< 0.1%
765.03 1
< 0.1%
708.87 1
< 0.1%
702.89 1
< 0.1%
690.07 1
< 0.1%
660.87 1
< 0.1%
637.47 1
< 0.1%
599.07 1
< 0.1%
590.9 1
< 0.1%
578.53 1
< 0.1%

lumpsum_investments
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6491
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.125876
Minimum0
Maximum4281.35
Zeros171
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-05-10T19:44:05.346168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.173
Q127.0975
median61.6
Q3117.285
95-th percentile266.0655
Maximum4281.35
Range4281.35
Interquartile range (IQR)90.1875

Descriptive statistics

Standard deviation104.29152
Coefficient of variation (CV)1.1701599
Kurtosis329.3675
Mean89.125876
Median Absolute Deviation (MAD)40.275
Skewness9.9928113
Sum724058.62
Variance10876.722
MonotonicityNot monotonic
2023-05-10T19:44:05.450941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 171
 
2.1%
4 19
 
0.2%
11 15
 
0.2%
6 11
 
0.1%
6.66 11
 
0.1%
5 10
 
0.1%
8 10
 
0.1%
4.5 10
 
0.1%
2 8
 
0.1%
10 8
 
0.1%
Other values (6481) 7851
96.6%
ValueCountFrequency (%)
0 171
2.1%
0.09 1
 
< 0.1%
0.17 1
 
< 0.1%
0.18 1
 
< 0.1%
0.19 4
 
< 0.1%
0.22 1
 
< 0.1%
0.29 1
 
< 0.1%
0.33 1
 
< 0.1%
0.34 4
 
< 0.1%
0.66 2
 
< 0.1%
ValueCountFrequency (%)
4281.35 1
< 0.1%
1113.73 1
< 0.1%
1059.36 1
< 0.1%
1056.8 1
< 0.1%
941.41 1
< 0.1%
879.17 1
< 0.1%
867.49 1
< 0.1%
807.69 1
< 0.1%
780.25 1
< 0.1%
765.75 1
< 0.1%

combined_purchase
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2009
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.449753
Minimum0
Maximum4306.42
Zeros3824
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-05-10T19:44:05.560881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.99
Q344.49
95-th percentile190.6635
Maximum4306.42
Range4306.42
Interquartile range (IQR)44.49

Descriptive statistics

Standard deviation111.45834
Coefficient of variation (CV)2.6256534
Kurtosis368.2225
Mean42.449753
Median Absolute Deviation (MAD)4.99
Skewness13.648152
Sum344861.79
Variance12422.961
MonotonicityNot monotonic
2023-05-10T19:44:05.669140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3824
47.1%
19.99 95
 
1.2%
11.99 62
 
0.8%
9.99 60
 
0.7%
14.99 49
 
0.6%
4.99 44
 
0.5%
24.99 43
 
0.5%
9.49 39
 
0.5%
15.99 38
 
0.5%
29.99 35
 
0.4%
Other values (1999) 3835
47.2%
ValueCountFrequency (%)
0 3824
47.1%
0.01 27
 
0.3%
0.02 7
 
0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.5 2
 
< 0.1%
0.51 10
 
0.1%
0.52 5
 
0.1%
0.8 1
 
< 0.1%
0.99 4
 
< 0.1%
ValueCountFrequency (%)
4306.42 1
< 0.1%
2828.79 1
< 0.1%
2142.62 1
< 0.1%
2033.85 1
< 0.1%
1768.42 1
< 0.1%
1624.58 1
< 0.1%
1508.02 1
< 0.1%
1470.85 1
< 0.1%
1418.89 1
< 0.1%
1063.69 1
< 0.1%

Interactions

2023-05-10T19:44:00.188950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:51.426083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.329015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.239596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.183584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.093546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.965645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.869254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.749586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.319698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.275965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:51.514546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.421492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.334482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.274234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.181138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.056916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.957736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.838111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.406619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.365827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:51.608217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.514268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.431202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.368263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.270815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.149929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.048017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.929170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.496417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.460957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:51.707800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.615185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.533040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.467443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.367174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.249311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.145100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:58.027408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.592295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.551332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:51.801296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.708606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.630423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.560282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.457893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.343596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.237015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:58.120568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.682594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.632915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:51.887203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.794799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.720070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.646627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.539720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.428785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.320492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:58.887717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.764303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.722028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:51.978981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.886644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.816899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.740203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.629458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.520085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.410679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:58.978088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.853381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.806164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.069217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.976419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.908983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.829318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.713952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.608268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.495393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.065011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.938014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.892385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.157929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.065392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.002715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.920084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.800787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.697721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.582412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.151545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.024398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.973932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:52.243704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:53.153761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:54.092618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.006790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:55.882586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:56.782918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:57.665543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:43:59.235395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T19:44:00.106174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-10T19:44:05.772765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
year_last_movedbalance_transferterm_depositavg_account_balanceportfolio_balanceinsuranceloansip_investmentslumpsum_investmentscombined_purchasenum_childrenage_bandmarital_statusoccupationoccupation_partnerhome_statusfamily_incomeself_employedself_employed_partnergenderregionnet_worth
year_last_moved1.0000.0080.0020.0040.0200.0110.0500.0110.0230.0270.1300.2070.1370.1370.1210.0470.1070.0360.0560.0000.0150.016
balance_transfer0.0081.0000.2250.4110.5320.4980.1860.3510.5980.1980.0000.0310.0110.0000.0000.0220.0000.0000.0000.0490.0210.015
term_deposit0.0020.2251.0000.2490.4400.4000.1960.2490.4950.3830.0000.0120.0130.0070.0000.0000.0170.0000.0000.0090.0080.013
avg_account_balance0.0040.4110.2491.0000.5770.4970.3030.4730.6630.2660.0100.0000.0290.0120.0000.0000.0000.0000.0090.0050.0140.104
portfolio_balance0.0200.5320.4400.5771.0000.7340.4150.5930.8730.4500.0300.0340.0000.0220.0000.0000.0000.0000.0000.0060.0000.083
insurance0.0110.4980.4000.4970.7341.0000.2900.4660.8370.3590.0170.0260.0160.0150.0000.0010.0000.0000.0230.0000.0070.156
loan0.0500.1860.1960.3030.4150.2901.0000.3160.4710.2150.0210.0090.0000.0170.0000.0000.0000.0000.0000.0000.0110.005
sip_investments0.0110.3510.2490.4730.5930.4660.3161.0000.6780.2660.0240.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.041
lumpsum_investments0.0230.5980.4950.6630.8730.8370.4710.6781.0000.5140.0100.0340.0000.0150.0000.0000.0000.0000.0000.0000.0070.053
combined_purchase0.0270.1980.3830.2660.4500.3590.2150.2660.5141.0000.0240.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.254
num_children0.1300.0000.0000.0100.0300.0170.0210.0240.0100.0241.0000.3110.1320.1890.1610.0220.1310.0630.1150.0440.0240.006
age_band0.2070.0310.0120.0000.0340.0260.0090.0000.0340.0000.3111.0000.2830.3200.2390.1890.1550.0940.1490.0530.0200.000
marital_status0.1370.0110.0130.0290.0000.0160.0000.0000.0000.0000.1320.2831.0000.1730.2480.1590.2250.0200.1260.0600.0310.000
occupation0.1370.0000.0070.0120.0220.0150.0170.0000.0150.0000.1890.3200.1731.0000.2730.0980.2210.3000.1680.2320.0400.000
occupation_partner0.1210.0000.0000.0000.0000.0000.0000.0000.0000.0000.1610.2390.2480.2731.0000.0730.1800.1500.3680.3250.0400.000
home_status0.0470.0220.0000.0000.0000.0010.0000.0000.0000.0000.0220.1890.1590.0980.0731.0000.1620.0300.0360.0370.0610.000
family_income0.1070.0000.0170.0000.0000.0000.0000.0000.0000.0000.1310.1550.2250.2210.1800.1621.0000.1200.1070.0920.0380.004
self_employed0.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0940.0200.3000.1500.0300.1201.0000.2400.1130.0440.000
self_employed_partner0.0560.0000.0000.0090.0000.0230.0000.0000.0000.0240.1150.1490.1260.1680.3680.0360.1070.2401.0000.1100.0230.000
gender0.0000.0490.0090.0050.0060.0000.0000.0190.0000.0000.0440.0530.0600.2320.3250.0370.0920.1130.1101.0000.0810.022
region0.0150.0210.0080.0140.0000.0070.0110.0000.0070.0000.0240.0200.0310.0400.0400.0610.0380.0440.0230.0811.0000.008
net_worth0.0160.0150.0130.1040.0830.1560.0050.0410.0530.2540.0060.0000.0000.0000.0000.0000.0040.0000.0000.0220.0081.000

Missing values

2023-05-10T19:44:01.122766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-10T19:44:01.411052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

num_childrenage_bandmarital_statusoccupationoccupation_partnerhome_statusfamily_incomeself_employedself_employed_partneryear_last_movedbalance_transferterm_depositavg_account_balancegenderregionportfolio_balancenet_worthinsuranceloansip_investmentslumpsum_investmentscombined_purchase
0231-35PartnerProfessionalProfessionalOwn Home>=35,000NoNo1981.0029.99312.25108.85FemaleNorth West360.372388.51230.87143.33369.3634.66
1045-50PartnerSecretarial/AdminProfessionalOwn Home>=35,000NoNo1997.0074.480.0048.45FemaleNorth West89.222110.9515.990.0087.4254.97
2136-40PartnerManual WorkerManual WorkerRent Privately<22,500, >=20,000YesYes1996.0024.460.000.00FemaleSouth West14.50218.440.0210.4615.150.00
3231-35PartnerManual WorkerManual WorkerOwn Home<25,000, >=22,500NoNo1990.000.000.000.00FemaleScotland68.98229.990.000.0020.0044.99
4055-60PartnerHousewifeProfessionalOwn Home>=35,000NoNo1989.000.000.000.00FemaleUnknown1.8820.000.009.983.320.00
5045-50PartnerSecretarial/AdminBusiness ManagerOwn Home>=35,000NoNo1984.000.010.000.00FemaleNorthern Ireland33.62256.4028.980.0032.249.49
6036-40PartnerSecretarial/AdminSecretarial/AdminOwn Home<30,000, >=27,500YesNo1986.000.000.0026.96FemaleWest Midlands13.1220.0026.9881.4247.3815.48
7061-65PartnerRetiredRetiredOwn Home<20,000, >=17,500NoNo1998.000.000.000.00MaleNorth West15.7420.000.0029.959.980.00
8145-50PartnerProfessionalProfessionalOwn Home>=35,000NoNo1980.0082.960.000.00FemaleUnknown36.05252.9628.970.0045.670.00
9336-40PartnerProfessionalHousewifeOwn Home<27,500, >=25,000YesNo1997.000.000.000.00MaleNorth West8.6020.0015.9924.4713.480.00
num_childrenage_bandmarital_statusoccupationoccupation_partnerhome_statusfamily_incomeself_employedself_employed_partneryear_last_movedbalance_transferterm_depositavg_account_balancegenderregionportfolio_balancenet_worthinsuranceloansip_investmentslumpsum_investmentscombined_purchase
8114145-50PartnerRetiredHousewifeOwn Home<12,500, >=10,000NoNo1982.00104.9827.9919.99MaleUnknown116.45199.470.000.0099.42148.45
8115236-40PartnerSecretarial/AdminBusiness ManagerOwn Home<27,500, >=25,000NoNo1986.0091.460.0028.46FemaleNorth114.382102.4460.4688.38117.8617.48
8116061-65PartnerRetiredRetiredOwn Home< 8,000, >= 4,000NoNo1982.0025.490.0035.98FemaleSouth East-7.3720.006.4811.9822.160.00
8117126-30PartnerSecretarial/AdminProfessionalOwn Home>=35,000NoNo1998.0067.9795.4681.45FemaleScotland91.37253.941.4949.9496.510.00
8118045-50PartnerProfessionalSecretarial/AdminOwn Home>=35,000NoNo1981.0015.9954.9888.92MaleNorth West210.662137.4433.43137.32162.9898.92
8119336-40PartnerManual WorkerHousewifeOwn Home<20,000, >=17,500NoNo1981.000.000.000.00MaleNorth West15.2320.000.000.000.000.00
8120061-65WidowedRetiredProfessionalOwn Home< 8,000, >= 4,000NoNo1960.000.009.492.99FemaleEast Midlands68.4220.000.0046.7618.480.00
8121141-45Single/Never MarriedHousewifeProfessionalRent from Council/HA< 8,000, >= 4,000NoNo1987.00107.420.0038.95FemaleNorthern Ireland106.06247.413.72102.3786.600.00
8122061-65PartnerRetiredRetiredOwn Home< 4,000NoNo1985.0059.480.000.00FemaleSouth East-9.1920.000.000.0011.900.00
8123341-45PartnerBusiness ManagerHousewifeOwn Home<25,000, >=22,500NoNo1974.0074.4742.9851.46MaleNorth160.792140.94100.3973.91165.1377.95